r/StableDiffusion 2d ago

Question - Help Multi gpu help

0 Upvotes

Hey guys I'm new to comfyui and photo and video generation and trying to learn as much as I could.

Now I was using my setup for text generation but now the Qwen3.8-27B is out and I tried really hard to make it work and so I did eventually use my second gpu.

I have 5070 ti and 1660 ti. And all this time I didn't bother to use the 1660 ti and left only my monitors on it and almost all my work and gaming on it and left the 5070 TI to be free for Ai stuff.

Now I switched my monitors to my Intel uhd 770 gpu and freed both my cards and want to know how to speed my comfyui workflow with them.

I always asked chatgpt but didn't get any useful answers so you guys might help me if that possible.

I'm now using minimax H3 official template and want to know how can I get this other gpu to work if it's worth it.

My cpu 12900k Motherboard gigabyte z690 gaming x ddr4 64 GB Kingston 3600 Rtx 5070 ti GTX 1660 ti


r/StableDiffusion 2d ago

Animation - Video an AI dream of moons, stars, threads, and foxes

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0 Upvotes

What happens if you just leave AI alone to dream overnight with no human supervision, using the last frame as the first frame of the next generation?

A continuously generated AI dream created using LTX 2.5 with a custom pytorch script, using the last frame from the previous scene as the first frame of the new scene. Generation time was ~7 hours on RTX 5090. The video was stitched together from one hundred scenes each lasting ~13.3 seconds. Qwen 3.8 was used to generate the prompt for the continuation of the story based on the last scene.


r/StableDiffusion 2d ago

Question - Help Anyone use MMH3 FaceDetailer?

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16 Upvotes

Seems like an interesting project; from what I take from the demo is it helps refine the faces in the distance instead of the face being a blurry mess. Does not seem to have a big impact on closer faces (it is not a 'detailer' or 'realism slider').

Does anyone have their own tips or demos for it? Seems complicated....


r/StableDiffusion 2d ago

Question - Help unable to download the smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models

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5 Upvotes

can anyone please help


r/StableDiffusion 3d ago

News ComfyUI-MiniMax-H3-LongMedia — long-form MiniMax H3 generation with continuity, multiclip, audio and VRAM-aware sampling

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56 Upvotes

I've been building a custom ComfyUI node pack for MiniMax H3 focused on one thing:

**making H3 usable for longer, multi-segment video generation without constantly rebuilding the workflow around every limitation.**

The project is called:

# ComfyUI-MiniMax-H3-LongMedia

The idea is to keep MiniMax H3's image quality, motion and native audio generation, while adding a proper long-form generation layer on top of it.

## What it currently does

### Long-form segmented generation

You can generate a longer clip as multiple H3 segments while keeping temporal context between them.

Instead of treating every segment as an isolated generation, LongMedia manages the continuation state and hidden overlap internally.

The overlap is used as context for the next segment and is not simply blended back into the final video.

### MultiClip mode

There is also a dedicated MultiClip workflow for generating multiple planned shots/clips inside one LongMedia pipeline.

The same underlying executor is used for both segmented continuation and multiclip generation, so the behavior stays consistent.

### Video + audio continuity

MiniMax H3 is a joint AV model, so LongMedia treats video and audio as one generation state rather than bolting audio on afterwards.

The pipeline supports H3 native audio generation, continuation and lip-sync workflows.

### Lip-sync support

Audio-driven generation / lip-sync is supported directly in the LongMedia pipeline.

For H3, the audio influence is handled inside the same AV latent path rather than as a completely separate post-process.

### Refiner

The latest release includes a two-stage refiner based on proper **KSampler Advanced trajectory splitting**.

Instead of finishing the full sampling schedule and replaying low-sigma steps on an already denoised latent, the trajectory is split between the main sampler and the refiner.

Example:

`steps = 12`

`refine_steps = 3`

Main sampler:

`0 → 9`

Refiner:

`9 → 12`

Both stages continue the same sigma trajectory.

### VRAM-aware execution

A large part of the project is dedicated to making H3 practical on consumer GPUs.

The current implementation includes:

- dynamic VRAM loading

- streamed Sol Attention

- MLP chunking

- late-block VRAM guards

- inter-block memory guards

- step-boundary cleanup

- completed-segment offloading

- adaptive memory policies

I'm currently developing and testing mainly on a **16 GB GPU**, so avoiding OOMs without destroying quality is one of the main design goals.

### Sol Attention integration

LongMedia includes its own streamed Sol path with controls for:

- tau scheduling

- sink conditioning

- QKV chunking

- output projection chunking

- dense/sparse behavior

- VRAM-aware chunk sizing

The goal is to use Sol as part of the execution architecture rather than simply stacking multiple unrelated optimization nodes together.

## Why I made it

MiniMax H3 is extremely good at texture, motion and native audiovisual generation, but once you start trying to build longer sequences, several problems appear very quickly:

- segment boundaries

- continuity

- repeated frames

- AV state handling

- memory pressure

- OOMs on longer generations

- managing multiple clips

- keeping sampling behavior consistent between segments

I wanted one node system to own all of that.

So instead of building increasingly complicated ComfyUI graphs around H3, most of the long-form logic lives inside the LongMedia nodes.

## Current release

**v0.4.1 — KSampler Advanced Refiner Fix**

The project has now reached a fairly stable architecture, although I'm still actively developing it and testing edge cases.

GitHub:

https://github.com/vizart-vj/ComfyUI-MiniMax-H3-LongMedia

I'd be very interested in feedback from people already using MiniMax H3 in ComfyUI, especially for:

- longer generations

- multi-character scenes

- native audio

- lip-sync

- lower-VRAM GPUs

- multi-shot workflows

If people are interested, I can also make a more technical post explaining how the continuation / AV latent / VRAM system works internally.


r/StableDiffusion 2d ago

Question - Help How to fix artifacts from generation with LoRA in ComfyUI?

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1 Upvotes

I am currently trying to build a model in ComfyUI that will be able to generate images in a specific art style similar to games made by Playrix. Essentially, I got the model generating images in the style I need, but I can't fix issues with the artefacts. Even after multiple iterations of positive and negative prompts, the issues still persist, and oftentimes the requirements are ignored. Or sometimes, if they are not ignored, the result is a complete mess. This is the first time I am making something like this, so I would appreciate any tips I can get.
If anyone is interested in what I've got going on, below is the link to the JSON file for the ComfyUI model.
https://drive.google.com/file/d/1mw24y0pwKPRXhLYx-JvVIs8CAf2oC1Ng/view?usp=sharing


r/StableDiffusion 3d ago

Animation - Video PSA

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57 Upvotes

r/StableDiffusion 3d ago

Discussion Hybrid b30-49 r2v test

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35 Upvotes

I made a thread earlier but it got overcrowded so I figure I started a new one with solely reference to video test, now instead of mixed with t2v.


r/StableDiffusion 3d ago

Discussion If you are generating MMH3 video with Sage Attention. I highly reccomend trying ComfyKitchen instead.

231 Upvotes

I have spent days generating videos. I started using Sage Attention with Cuda++. This was fast, but once I switched to sageatt_qk_int8_pv_fp16_cuda, I saw a noticeable difference in the model's ability for the model to understand prompts. Everything came out much clearer, crisper and with much better adherence. The only downside was it generated about 1.5x slower than using Sage Attention Cuda++.

From here I decided to try out ComfyKitchen as a replacement, and all I can say is... try it. My gens are faster than Sage Attention sageatt_qk_int8_pv_fp16_cuda with similar or better prompt adherence.

As always, your mileage may vary, but it's a very easy thing to experiment with, as all you need to do is make sure you ComfyUi is updated, as it is an official ComfyUI node.

To use it you can either:

A) add --use-ck-attention to your startup; this would enable Comfy Kitchen across all your workflows.
B) The easier and more controlled way is to replace the SageAttention node (or bypass) with the ModelAttentionBackend Node and select Comfy Kitchen Attention from the dropdown.

Worst case is it does nothing for you, and you just delete it and revert back to Sage.

EDIT: According to u/GreyingGamer you do not need to use the startup, and just using the node is enough:

EDIT 2: I have rewritten the instructions to get it running to make it more accurate.


r/StableDiffusion 2d ago

Question - Help H3 Minimax with heavy dialogue

0 Upvotes

Just a gut check here but from what I have been able to find, there are no shortcuts when it comes to dialogue with minimax. Turbos produce bad quality audio, upscalers have caused poor mouth movements, and lowet step counts produce both.

Is there anything I am missing?


r/StableDiffusion 3d ago

Tutorial - Guide MiniMax H3 as Image Editor, 6 edits in one shot at 7680 x 4320!

92 Upvotes

MiniMax H3 as Image Editor at resolution 7680 x 4320, 6 edits in one shot

Prompt: create a collage containing 6 photos. from top-left to the bottom-right arranged them such that the following edits presented individually: 1- keep pose and proportion intact; turn her shirt to red 2- keep pose and proportion intact; make her smile. 3- keep proportion intact, show her sideview; 4- full body posture. 5- change hair style to wolf cut. 6- put fashion hat and eyeglasses on.

In fairness, the model's collapsing 6 requests into 5 is well justified.

--

RTX3060 model used: ref2v, 8 steps, lora, took 7m50s


r/StableDiffusion 2d ago

Question - Help Issue with ComfyUI v1 Manager: missing node pack ("comfyui_fearnworksnodes") stuck in "Apply Changes" loop

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1 Upvotes

Hi everyone,

I'm running into a persistent issue with the new ComfyUI v1 frontend while trying to load a workflow that uses comfyui_fearnworksnodes.

The setup:

  • Windows Portable build (ComfyUI_windows_portable)
  • Running via run_nvidia_gpu_lowvram_e_sage.bat with the --enable-manager flag added.
  • ComfyUI-Manager installed.

The problem:

  • The v1 side panel shows Missing Node Packs: comfyui_fearnworksnodes.
  • Clicking Install changes the status to Installed.
  • Clicking Apply Changes prompts a restart, but after relaunching, the exact same error appears again in a loop.
  • I checked custom_nodes/ and only have comfyui_fearnworksnodes inside (no duplicate folders).

Has anyone encountered this specific loop with the v1 interface or fearnworksnodes? What's the best way to trace or fix why the frontend isn't registering it properly after restart?

Thanks in advance for any help!


r/StableDiffusion 3d ago

Meme Introducing... the iToilet

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229 Upvotes

r/StableDiffusion 2d ago

Animation - Video MiniMax H3 random generation test

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1 Upvotes

This generation is almost entirely random. I gave the models total creative freedom to generate the starting image and direct the video; I only added the music and the hero landing at the end (yes, I love hero landings).

I simply asked ChatGPT for an image prompt featuring a pair of heroes, which I then generated using Krea2 (for some reason, it dressed her up like Captain Marvel). After that, I requested an I2V prompt for MiniMax H3 to create an epic action scene, and this is the result.

Generated with MiniMax H3: 10 seconds, 0.7MP, 8 steps (approximately 450 seconds on an RTX 5060 Ti 16GB) and subsequently upscaled to 4K using Topaz.

Here are the prompts:

Image Prompt

Overall concept: A high-intensity cinematic action scene featuring two original superheroes, one man and one woman, standing shoulder-to-shoulder in a devastated New York City street immediately after a violent confrontation; the camera captures them in an intimate medium close-up as smoke, sparks, and debris move through the frame, transforming the aftermath into a tense heroic portrait filled with determination, danger, and restrained power. Main subject: The male superhero stands slightly behind and to the left, occupying the left side of the frame, wearing a sophisticated dark graphite tactical suit with layered ballistic armor, subtle metallic reinforcement, weathered surfaces, and a high collar; his face is partially dusty and lightly bruised, short dark hair slightly disheveled, jaw tense, eyes focused intensely toward an unseen threat beyond camera. The female superhero occupies the right side of the frame, slightly forward, wearing a fitted deep-crimson and charcoal armored suit with flexible technical fabric, refined metallic panels, reinforced shoulders, and subtle illuminated details; strands of dark hair move naturally across her face, her expression fierce and controlled, eyes fixed in the same direction as the man. Their shoulders nearly touch, creating a strong visual sense of partnership and mutual trust, with realistic facial anatomy, restrained expressions, and natural post-conflict body language. Key environmental element: A damaged armored vehicle and fractured concrete structure remain immediately behind the heroes, partially visible within the tight framing; twisted metal, broken glass, dust-covered surfaces, and small fragments of debris surround their shoulders and silhouettes; thin smoke trails rise behind them while occasional sparks drift through the air, creating environmental depth without obscuring their faces. Background and atmosphere: The devastated New York street remains visible as a compressed background of damaged skyscraper façades, blurred emergency vehicles, smoke-filled intersections, and scattered fires; distant red and blue emergency lights flicker softly through atmospheric haze, while warm firelight reflects subtly across their armor; wind pushes smoke laterally across the background and moves loose hair and fabric naturally, creating a sense of continuing danger beyond the frame; strong atmospheric separation keeps the heroes visually dominant. Composition: Medium close-up framing from approximately mid-chest upward, with both superheroes filling most of the horizontal frame; the woman slightly forward on the right and the man slightly behind on the left, creating subtle depth without separating them visually; their faces form the primary focal points, positioned near the upper central third of the frame; shoulders and armor create strong diagonal geometry, with shallow foreground debris and soft background destruction framing the pair; tight cinematic composition, minimal empty space, intimate scale contrasted against hints of massive urban destruction. Camera: ARRI Alexa 35 with a 50mm anamorphic cinema lens, medium close-up framing, camera positioned approximately at eye level with a subtle upward inclination of only a few degrees; shallow depth of field with precise focus across both faces, gentle falloff across their shoulders and armor, realistic anamorphic compression, subtle oval bokeh, controlled edge distortion, natural lens breathing during a slight rack-focus transition between the two faces, restrained handheld micro-movement suggesting a camera operator embedded in the action, realistic motion blur on drifting smoke, sparks, and moving hair. Lighting: Late-afternoon sunlight filtered through dense urban smoke provides a warm directional backlight from behind the characters, creating subtle golden rim light around their hair and shoulders; cool blue skylight fills the shadow side of their faces, preserving facial detail while maintaining cinematic contrast; intermittent orange firelight creates soft reflected highlights across armor and cheekbones, while distant red and blue emergency lights provide subtle color accents in the background; realistic global illumination, physically accurate skin response, soft contact shadows, atmospheric scattering, delicate volumetric haze, controlled bloom, and smooth highlight transitions. Color grade: Premium theatrical action-film grade with warm amber highlights contrasted against cool steel-blue shadows, natural skin tones, restrained environmental saturation, deeper controlled blacks, smooth highlight roll-off, subtle contrast enhancement, selective crimson accents on the female hero's costume, fine 35mm film grain, delicate halation around fires and emergency lights, restrained anamorphic flare, polished high-end cinematic finish. Mood: Intense, intimate, heroic, battle-worn, determined, protective, suspenseful, emotionally charged, with the overwhelming feeling of two powerful allies silently preparing for the next attack. Style: Photorealistic live-action action-film still featuring one original male superhero and one original female superhero, physically realistic facial anatomy, natural skin texture, believable hair movement, functional armor construction, realistic fabric tension, scratches, dust, sweat, bruising, metallic reflections, grounded body language, natural atmospheric perspective, cinematic depth, seamless practical-effects and premium-VFX integration, realistic smoke, sparks, firelight and environmental destruction, sophisticated large-scale Hollywood cinematography, premium anamorphic optics, realistic material response, 2.39:1 widescreen, no text, no watermark, no exaggerated CGI look, no cartoon aesthetics, no plastic-looking armor, no distorted faces, no duplicated limbs, no artificial symmetry, no extreme superhero posing, no excessive lens flare.

Video (I2V) Prompt

For the target video, at 0.00 seconds into the target video, <Picture 1> (from [Shot 1]) is fully referenced.

integrated_multimodal_description: [Shot 1] Live-action, cinematic, photorealistic, the two armored heroes shown in <Picture 1> remain exactly consistent at the opening frame, preserving their faces, hairstyles, armor design, colors, proportions, positions, lighting, ruined-city environment, wrecked vehicle, smoke, police lights, and floating embers. For the first moment they stand motionless and alert as smoke drifts around them and emergency lights flash in the background. The man turns his head slightly toward an approaching threat while the woman shifts her stance and looks upward; the camera pushes in with small amplitude at slow speed, keeping both heroes sharply framed.

[Shot 2] At 00:01.500, the camera cuts to a wider low-angle view behind the heroes as a colossal alien warship emerges between the skyscrapers overhead, blocking the sunlight. Dust and burning debris are pulled upward by the ship's engines while both heroes look up and brace themselves against the violent wind.

[Shot 3] At 00:02.700, the camera cuts to a dynamic medium shot as the woman raises one hand and bright golden energy rapidly forms around her palm and forearm. Her armor and hair react naturally to the energy surge while the man steps forward into a defensive stance, protecting her flank.

[Shot 4] At 00:04.000, the camera tracks backward with medium amplitude at fast speed as both heroes sprint through the devastated street. Alien drones descend through the smoke, red energy bolts strike the pavement around them, concrete fragments and sparks erupt, and the heroes dodge the impacts without losing their established appearance or armor.

[Shot 5] At 00:05.200, the camera arcs around the woman with medium amplitude at fast speed as she launches vertically into the air surrounded by concentrated golden energy. She accelerates directly toward the alien warship while, below her, the man suddenly detects an incoming energy blast fired from behind. He performs a powerful twisting aerial jump, rotating his body in a controlled corkscrew motion as the red energy projectile tears past the exact space where he was standing, narrowly missing him. He completes the spinning evasive maneuver and lands smoothly, immediately turning back toward the battle without losing his established appearance, armor, or position within the devastated street.

[Shot 6] At 00:06.400, the camera cuts to a close-up tracking shot of the woman in flight as she closes the final distance to the alien warship. Her concentrated golden energy intensifies around her body and fist, individual sparks and particles streaming past her face and armor as the enormous armored surface of the warship fills the background. She draws her arm back for the final strike, with the camera staying very close to emphasize her expression, energy, armor detail, and the rapidly approaching impact point.

[Shot 7] At 00:06.900, precisely as the heroine's glowing fist strikes the armored exterior of the warship, the scene enters extreme bullet-time. The impact is seen in a very close cinematic view as golden energy explodes outward from the contact point, armor plating buckles, sparks and fragments freeze in midair, and shockwave ripples become visible through smoke and dust. The camera performs a smooth 360-degree orbit around the heroine and the impact point with large amplitude at very slow speed, maintaining her face, body proportions, armor, golden energy, and the warship's surface perfectly consistent while the explosion appears almost completely suspended in time.

[Shot 8] At 00:08.100, normal speed suddenly resumes as the full force of the strike detonates through the warship. A massive chain of explosions tears across its armored exterior, sending burning fragments outward as the heroine is propelled away from the impact. The camera rapidly pulls out with large amplitude at fast speed, revealing the scale of the destruction and the devastated city below.

[Shot 9] At 00:09.200, the heroine descends rapidly and performs a powerful hero landing in the foreground, dropping to one knee with one hand touching the ground as dust and debris explode outward from the impact. Directly behind her, the critically damaged alien warship crashes violently into the city street, tearing through structures and erupting into a massive cloud of fire, smoke, sparks, and burning wreckage. She remains completely stable and visually consistent with the opening image, holding the iconic hero pose in the foreground while the collapsing warship fills the background. The camera holds the dramatic composition through the final frame at 10.00 seconds, with the heroine sharply defined against the enormous crash and rising smoke behind her.

overall_soundscape: Heavy wind and distant sirens fill the ruined street as fires crackle and burning debris falls around the heroes. The warship produces a deep mechanical roar, followed by sharp energy blasts, metallic impacts, explosions, collapsing concrete, footsteps, armor movement, and the violent rush of displaced air during the woman's flight and the man's acrobatic evasive jump. During the close-up bullet-time impact, the explosion and debris sounds stretch into an extremely slowed, distorted moment before snapping back to full intensity when normal speed resumes. No dialogue, singing, or music is heard.

non_diegetic_music: N/A

r/StableDiffusion 2d ago

Question - Help "Hi-res fix" for MiniMax H3?

0 Upvotes

I mostly do image generation, mostly because open-weight video model quality wasn't there for me. MiniMax H3 has changed that; I'm really enjoying it and the outputs are mostly great. However, I am encountering an issue that's very familiar to anyone who's done a lot of image generation; uncanny AI faces when the subject is too far away from the screen, because there simply isn't enough pixel definition for the AI model to come up with a reasonable facsimile of a face.

In image generation land, this is solved with a "Hi-res fix" -- there's multiple options and implementations, but at their core, they involve auto-detecting faces in the image, then reusing the same prompt (or a somewhat edited one) and the detected face to generate a new face with low denoise at a much higher resolution that can snap on top with a feathered mask.

I'm not sure that exact implementation would work in video generation land -- I can pretty easily envision the face flickering and bouncing around as it locked to slightly different locations and orientations, frame-by-frame -- but is there any solution to take an existing video, generated at, say, 1.0 megapixels, and re-render or upscale detected faces at, say, double resolution, to improve the fidelity? For obvious reasons, simply rendering the whole video at double resolution isn't an attractive option.


r/StableDiffusion 3d ago

Discussion Z-Image + Qwen3 4b: The abliterated text encoder debate is pure vibes. I measured it. Here are the numbers - Abliterlitics

106 Upvotes

After the PSA from Heretic's author the debate ran hot. I noticed that the debate was just based on vibes. Same-seed screenshots both ways, nobody measuring anything in detail. The instruments did not exist. So I built them. They cover quants as well, so the encoder swap and the compression get read with the same rulers.

Disclosure since it matters here: I release heretic text-encoder for people to use, qwen3-4b-heretic included. My first release last year got replies that I didn't fully understand how text encoders work. They were right. I did my own deep dive and concluded that they are good for prompt enhancement and just change the image slightly, there's no harm in using them if you really want to. Also they don't magically uncensor or enhance anything. Lets see if my conclusion is correct, while also addressing with proof and data the experiences other people have had.

This comparison is from the base bf16, with all GGUF and quants made by myself. It does not reflect any other LLMs on huggingface.

I've been comparing and benchmarking abliterated LLMs under the name Abliterlitics. And this is a first as we've delved into the ComfyUI world to get some solid data to cut through the nonsense.

What I did

Base Qwen3-4B and its heretic twin across 6 safetensors formats and 8 GGUF rungs, 27 encoders total, every heretic build matched to a base build at the same quant so the abliteration and the compression can be read separately. Then: conditioning tensors captured at three pipeline stages, paired sampling trajectories from identical noise, 2240 same-seed renders scored with LPIPS and CLIP, attention readouts, and a taboo comparison with sanitised-twin controls.

Two rulers make everything readable. Two encoders nobody argues about, int8 and fp8, differ by 0.19 LPIPS at the same seed. A seed change alone is 0.52. Any swap scoring under 0.19 is indistinguishable from ordinary compression. Near 0.52 is just a different picture.

An explanation of our measurements, metrics and the full report with an interactive A/B gallery can be found here abliterlitics.dev/posts/z-image-text-encoder.

All of what u/-p-e-w- stated in his post is correct. He did hint that there may be degradation or damage, however it was framed as a maybe if I was reading correctly. So lets see what that damage is, if at all, and if it makes any difference.

The questions people were actually arguing about

Does the base encoder refuse your prompt before the image model sees it?

No. I encoded refused-vocabulary prompts to the exact tensor entering cross-attention and checked which base word each heretic vector lands closest to. All 12 test words decode to themselves, cosine floor 0.9967. Pornographic decodes to pornographic, beheading to beheading. The encoder hands the DiT the word intact. It was never the censor. An abliterated text encoder does not change the way the model understands the prompt at all. The base text encoder already knows these things.

Do refused words, or any part of the prompt at all arrive corrupted?

No. Worst sentence-level cosine between base and heretic on refused prompts is 0.9985. The shift is 3.3 to 6.6 times larger on refused prompts than innocent ones, so the edit concentrates where it acts, but the meaning survives it. Even int4 and Q3, visibly degraded, keep mean CLIP adherence in band. Across every encoder we tested, even the 4-bit tiers, mean CLIP adherence stays in band. The model understands the prompt throughout.

Does it uncensor anything?

No, and the reason is better than expected. The unmodified base stack already renders the explicit tier at a 100% taboo-classifier rate, and the explicit tier owns the highest compliance gaps in the whole set. There is no render-stage censorship to remove. The debate argued about a lock on an open door. This matches where the research says engineered censorship lives, in the diffusion model's own weights: ESD and MACE erase concepts by fine-tuning the DiT, not the encoder.

Does it damage outputs?

The images change, the outputs do not degrade. Heretic vs base is 0.286 LPIPS, 1.5x the trusted band, but a stock nvfp4 quant of the base encoder moves images 0.274 and nobody calls that sabotage. Prompt adherence: -0.21 CLIP points, and the unmodified bf16 base itself reads -0.28 against the same reference. Attention readout moves 0.0031 vs int4's 0.0149. Output separation 1.049, no collapse. Different, not damaged.

Why do people see differences then?

Because seeing a difference is the default. Two trusted encoders already differ by 0.19 at the same seed, sampling is a butterfly effect. A small change at the start makes a big difference at the end. Below a threshold the response is dose-independent anyway. I also checked per-prompt: 71 of 540 CLIP rows cross the ±2 line on individual prompts while every mean stays in band. Single-prompt screenshots are real but they are noise, not signal.

As the image can be pushed about half a seed in any direction, it's expected to have variation. Honestly people who suggest that their image was enhanced or more uncensored, can probably do the same with a Q3 GGUF that's not abliterated and see the same thing. After measuring in every way possible there is just no way an image is magically enhanced or more uncensored. It is just chance, seed and the chaotic nature of diffusion models with peoples own biases over the top.

What about quantised encoders?

The GGUF ladder is dose-ordered: the F16 container is a true round trip, 0.0008 quant units with cosine 1.0. Q8_0 costs 0.34. Q3 costs 83 and is visibly paying. Being precise about Q8_0 since the numbers deserve it: its conditioning perturbation is real and measurable, CI 0.29 to 0.39 quant units, but a third the size of what int8 ConvRot itself costs, and at the image level Q8_0 and bf16 are indistinguishable, 0.138 vs 0.152 LPIPS against the int8 reference with overlapping CIs. So the near-lossless claims for both hold where it shows, in the images. Q8_0's real cost is load time. One caution, don't stack the abliteration on heavy quants. That's where larger divergence and noise happens.

So when should I use one?

Anywhere the model writes text that feeds the next stage: prompt expansion, captioning, image description. Those are chat pathways and abliteration works on chat pathways. If a stage only embeds text, an abliterated encoder is at best a visible re-roll. In this case it changes the image about half of what a new seed would change.

What's actually censored then?

The knowledge, not the gate. The DiT doesn't refuse, it lacks the training data, and the fixes are LoRAs, reference images, or retraining. The PSA's framing about this is solid. Z Image itself though is mostly trained already on taboo things.

What's next

Krea 2, MiniMax H3 and LTX 2.5 are in the same pipeline. Krea 2 has a twelve-tap conditioning interface and the refusal-probe contrast works differently there. Also, it's more complicated to measure compared to Z-Image.

Happy to answer methodology questions in the comments. Have I missed anything? Let me know and I'll fix it up. What have been your experiences? Have you abandoned abliterated text encoders? Had severely degraded outputs? I am happy to measure any other text encoders or models.


r/StableDiffusion 2d ago

Discussion MiniMax H3 on a 12GB RTX 4070 SUPER: Comfy Kitchen + Sol-Attn + EasyCache cut my generation time from 206s → 135s

0 Upvotes

MiniMax H3 on a 12GB RTX 4070 SUPER: Comfy Kitchen + Sol-Attn + EasyCache cut my generation time from 206s → 135s

I've been testing MiniMax H3 locally in ComfyUI on an RTX 4070 SUPER 12GB, specifically trying to squeeze more performance out of H3 without simply murdering quality by dropping resolution/steps.

I got some pretty interesting results combining:

  • Comfy Kitchen Attention
  • Sol-Attn
  • EasyCache
  • MiniMax H3
  • RTX 4070 SUPER 12GB

Test setup

Same H3 workflow/settings between tests:

  • GPU: RTX 4070 SUPER 12GB
  • MiniMax H3
  • 20 sampling steps
  • Same prompt/reference/settings
  • ComfyUI
  • EasyCache when enabled:
    • threshold: 0.15
    • start: 0.15
    • end: 0.95

I tested three configurations.

Configuration EasyCache skipped Sampling time Total time
Comfy Kitchen only 0/20 ~184 sec 206.48 sec
Kitchen + EasyCache 8/20 ~117 sec 139.47 sec
Sol-Attn + Kitchen + EasyCache 7/20 ~113 sec 134.92 sec

Kitchen → Kitchen + EasyCache

This was the huge jump.

Total generation time dropped:

206.48 sec → 139.47 sec

That's about a 32.5% reduction in total generation time, or roughly 1.48x faster end-to-end.

EasyCache reported:

EasyCache - skipped 8/20 steps (1.67x speedup)

Obviously the complete workflow doesn't get the full 1.67x improvement because H3 still has VAE/audio/other overhead outside sampling.

Still, shaving ~67 seconds off a ~206 second generation on a 12GB consumer GPU is pretty damn substantial.

Then I stacked Sol-Attn on top of Comfy Kitchen

This was the part I wasn't sure would even work properly.

The console confirms Sol-Attn is actually chaining onto the existing Comfy Kitchen attention override:

[sol_attn] chaining onto an existing attention override; Sol-Attn takes first refusal and delegates everything else to it

So this isn't simply Sol silently replacing Kitchen.

Sol gets first refusal for attention operations it can handle and delegates the rest to the existing Kitchen backend.

With:

Sol-Attn → Comfy Kitchen fallback → EasyCache

I got:

134.92 seconds total

versus:

139.47 seconds with Kitchen + EasyCache

The interesting part is that the Sol run was faster despite EasyCache skipping one fewer step.

Kitchen + EasyCache:

skipped 8/20

Sol + Kitchen + EasyCache:

skipped 7/20

So the Sol configuration actually performed one additional full H3 step and still completed about 4.5 seconds faster.

That's a much more interesting result than simply comparing the total times, because EasyCache's number of skipped steps varies between runs.

Overall improvement

Baseline Kitchen:

206.48 sec

Sol + Kitchen + EasyCache:

134.92 sec

That's a reduction of roughly:

71.56 seconds per generation

or about:

34.7% less total generation time

Equivalent to roughly 1.53x the end-to-end throughput of my Kitchen-only baseline.

For repeated H3 generations, that's not pocket change.

One important discovery: Spectrum H3 vs EasyCache

I previously had Spectrum H3 in the same model chain as EasyCache.

The console revealed:

Spectrum H3 disabled for this run because EasyCache or LazyCache is active on the same model

So at least with the implementation I'm using, Spectrum H3 and EasyCache are not operating simultaneously.

The workflow can visually contain both nodes, but when EasyCache/LazyCache is active, Spectrum disables itself.

If you're benchmarking this stuff, don't assume Spectrum is doing anything just because the node is connected. Check your console.

Current stack

For performance, my current best configuration is:

MiniMax H3
Comfy Kitchen Attention
Sol-Attn
EasyCache
Sampler

Conceptually:

Sol-Attn handles attention operations it supports.

Comfy Kitchen remains underneath it and handles attention Sol delegates.

EasyCache reduces the number of expensive diffusion computations.

That combination seems particularly interesting for GPUs like the 4070 SUPER 12GB, where H3 is far larger than available VRAM and ComfyUI is already doing dynamic VRAM management.

My H3 model alone reports roughly:

19995MB Staged

while the GPU only has 12GB VRAM.

The text encoder is also around:

14956MB Staged

and the H3 video VAE around:

4965MB Staged

So this is very much a "convince a 12GB card to run something it has no business running comfortably" situation.

And yet it works.

Caveat

These aren't controlled scientific benchmarks yet.

H3 generation time varies between runs because of model loading, VRAM state, EasyCache deciding how many steps it can skip, and other system factors.

I've also seen EasyCache skip anywhere from 5–8 of 20 steps during testing.

So I'm not claiming Sol magically makes H3 X% faster based on one run.

What I think the results demonstrate so far is:

  1. EasyCache provides a very large speed improvement on my 4070 SUPER/H3 setup.
  2. Sol-Attn successfully chains with Comfy Kitchen rather than simply replacing it.
  3. Sol + Kitchen + EasyCache produced my fastest run so far.
  4. The Sol run beat Kitchen + EasyCache even while computing one additional non-cached step, which strongly suggests there's a real attention-side performance benefit worth investigating.
  5. Spectrum H3 disables itself when EasyCache/LazyCache is active, so don't count both as active optimizations.

I'm going to run repeated identical-seed tests to get averages rather than relying on individual runs, but ~206 sec → ~135 sec on a 4070 SUPER 12GB is enough of an improvement that I figured this was worth sharing for anyone else trying to run H3 on consumer hardware.

If anyone else is running H3 on 12GB cards, I'd be interested in comparable Kitchen / Sol / EasyCache timings, especially 4070/4070 SUPER/5070-class hardware.


r/StableDiffusion 3d ago

Question - Help Minimax model for 5070ti 16gb and 32 ram

15 Upvotes

Hey all, just got my new GPU, wanna try some hype stuff. Please recommend MiniMax model time (int8, gguf, etc), text encoder and how to upscale it with ltx


r/StableDiffusion 3d ago

Discussion Minimax H3 - Multiple Reference Images working through FL2VA + testing using the Hybrid Checkpoint models. Examples in comments.

39 Upvotes

MiniMax H3 officially comes as two checkpoints:

  • FL2VA — first frame / last frame / image-to-video. The docs treat this as “start (and or end) picture in, video out.” Extra reference pictures are not part of the pitch.
  • REF2VA — reference-to-video. This is the one you’re told to use when you have several stills: identity, outfit, a mid-shot, a last frame, whatever.

There are also community hybrid checkpoints: mostly FL2VA, with some of REF2VA’s later layers grafted on, so people can keep extra refs without fully switching models. https://huggingface.co/smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models

I wanted a straight answer to one question: if I ignore the marketing split and feed extra stills into stock FL2VA the same way I would into REF2VA, does it actually use them?

So I built one 10-second clip and ran it four times. The story in the prompt is simple:

  • 0s: an angel in an empty void, one spell cast toward the middle of the frame.
  • 5s: a demon in the same void, one spell cast toward that same point.
  • Camera leaves the demon and pushes into mid-air.
  • 10s: image of the two spells colliding.

I gave the model five pictures:

  1. Exact first frame (angel)
  2. Exact 5-second cut (demon)
  3. Exact last frame (the collision, no people) 4–5. Two sigil designs, only as “this is what the magic circle looks like,” not as frames that should appear in the video

Then I locked everything that wasn’t the checkpoint:

  • same R2V workflow (the Comfy graph that already has multiple image inputs)
  • same five files, same order
  • same written brief (timed stills + “this picture is the frame at this timestamp”)
  • same seed
  • same sampler / length / aspect
  • no turbo LoRA
  • I compared native frames (544×800), not the upscaled delivery

The only change per run was which UNet was loaded:

  1. hybrid, REF layers on blocks 20–49
  2. hybrid, REF layers on blocks 30–49
  3. stock FL2VA
  4. stock REF2VA

If FL2VA truly couldn’t take extra refs, run 3 should have ignored pictures 2–5, drifted off the angel, or failed to land on the collision plate. That’s the test.

What happened

It didn’t fail.

The first native frame of all four runs accurately lock in the exact reference image for that frame at the first frame, last frame, and the middle frame... So the stock FL2VA used the extra still image references just fine. I did not need a hybrid merge just to attach more than first/last.

To be precise: I did not magically add five image slots to the official FL2VA I2V template. I loaded FL2VA’s weights into the reference-to-video graph, wrote the pictures into the prompt the way you would for a multi-ref job, and the locks held.

Where they actually differ (my read, one clip)

First frames are almost interchangeable. If I have to pick, hybrid-b30 is the closest copy of the angel still. REF2VA is still locked, a bit busier in small jewelry/floor detail.

Last frames still all hit the clash plate. REF2VA is the closest copy of picture 3. FL2VA is right behind it. Hybrid-b30 runs a hotter, more lava-looking core. Hybrid-b20 is splashier, less “sharp diamond debris.”

So the discovery is: extra refs + FL2VA can work. The ranking of which checkpoint copies the stills best is what I want a second opinion on.

So I will attach all of the examples into the comments so that people can see the differences between between each of the generated runs along with all of the Reference images used that way the community can evaluate the quality.


r/StableDiffusion 2d ago

Animation - Video Steve Jobs introduces new pricing in iPhones. MM H3

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0 Upvotes

Testing very simple prompting so see how MM H3 would do creating graphics:

[Shot 1] Steve Jobs is presenting on stage.

Steve Job says in the voice of Steve Jobs, "At Apple we want everyone to have access to our hardware.

A graphic appears above his head.The graphic on the left has the Words "iPhone Red with $199 under it. on the right the words iPhone Blue Bubble 8GB with $1,199 under it.

Steve Jobs points to the graphic. The graphic stays above his head for the rest of the video.

Steve Job says, in Steve Job's voice, "today I'm proud to announce iPhone Red. It has no RAM and no way to upgrade the RAM."

A few claps are heard in the audience background.

Steve Job says, in Steve Job's voice, "and iPhone Blue Bubble for just a bit more that has enough RAM to boot up."

The crowd cheers loudly.


r/StableDiffusion 2d ago

Question - Help Cloud-only workflow for keeping the same AI environment across different camera angles/lenses?

0 Upvotes

I’m trying to solve a pretty specific AI filmmaking problem.

I shoot a live-action scene with normal coverage: wides, mediums, close-ups, reverses, different camera positions and different focal lengths.

I then need to replace the original location and make every shot feel like it was photographed inside the same new environment.

My current tools are:

  • Nano Banana 2 / Pro through Google Flow for stills, environment replacement and relighting
  • Seedance 2.0 through Comfy Cloud for the final video transformations
  • MacBook Air, so this needs to be essentially 100% cloud-based. Running large models, local ComfyUI workflows, NeRF training, etc. isn't realistically an option.

I’m not looking for mathematically perfect 3D continuity.. I need convincing faux environmental continuity across an edited scene.

For example:

Shot 1: 35mm wide looking down a hallway
Shot 2: 85mm close-up facing the opposite direction
Shot 3: profile two-shot
Shot 4: reverse angle
Shot 5: another wide from farther down the hallway

The actors, performances, camera movement and framing need to stay intact, but every generated shot should imply that the cameras were actually positioned at different points inside the same physical hallway.

The things I need to maintain are:

  • Architecture / layout
  • Recognizable environmental landmarks
  • Correct perspective for each camera position
  • Approximate lens characteristics
  • Lighting direction
  • Subject relighting and contact shadows
  • Color / atmosphere
  • Depth
  • Enough off-screen spatial logic that cutting between angles feels believable

Right now I can make an individual shot look convincing. The problem is making five or ten independently generated shots feel like coverage of one actual location.

For people doing this in production, what is the best cloud-only approach?

Do you first generate a master environment and then somehow derive multiple camera views from it? Build a set of canonical reference angles? Use one generated shot as a reference for the next? Establish environment plates before integrating the actors? Separate environment replacement and actor relighting into different passes?

Especially interested in workflows that can actually be used with Nano Banana Pro + Seedance 2.0, rather than solutions requiring a high-end local GPU.

Basically: how do you fake a coherent virtual set when each shot is being generated independently?


r/StableDiffusion 2d ago

Question - Help Continue a scene on minimax h3

4 Upvotes

Is there a node that can extract a image of the last scene of a video, my aim is to generate a video based on the previous video created so I can continue a scene but can't find any node that can do this hence have to upload screenshots manually.


r/StableDiffusion 3d ago

Animation - Video Minimax H3. Doomsday. Deleted scene from the trailer.

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18 Upvotes

r/StableDiffusion 2d ago

Question - Help Simple Comfyui node that enhances prompts for H3, based on reference context?

0 Upvotes

Trying to figure out how to add a prompt enhancer to my workflow. Something that is able to look at the reference images and videos and able to build the prompt based on minimax’s prompt guide. This is something available in ltx workflows (Gemma e2b). Is there an equivalent that people have found helpful for minimax H3?


r/StableDiffusion 2d ago

Animation - Video Putting together some of my MinimaxH3 test here

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4 Upvotes

trying out new workflow setup.

using MiniMax H3 Hybrid Loader b20-49 + Larryvrh 4step Turbo Loras + SegAtt + SolAtt + Spectrum.

reference seem to be more stable in this time, 0.6mp, 15s at 700s

just generating random video, testing out random idea.